Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model

In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tanβ, b and θ was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability

Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm

The probability integral method (PIM) is the main method for mining subsidence prediction in China. Parameter errors and model errors are the main sources of error in the application of the probability integral method. There are many surface subsidence problems caused by coal mining. In order to improve the accuracy and operating efficiency of the genetic algorithm (GA) in calculating the parameters of the PIM, this paper proposes an improved genetic algorithm (IGA) by adding the dynamic crossover and mutation rate to the traditional GA. Made improvements to the shortcomings of random crossover and mutation rate of all individuals in the population in the original algorithm.Through simulation experiments, it is confirmed that the IGA improves the calculation efficiency and accuracy of the traditional GA under the same conditions.The IGA has higher accuracy, reliability, resistance to gross interference and resistance to missing observation points. This method is obviously superior to direct inversion and conventional optimization inversion algorithms, and effectively avoids the dependence on the initial value of the probabilistic integral method parameter

Co-reductive fabrication of carbon nanodots with high quantum yield for bioimaging of bacteria

A simple and straightforward synthetic approach for carbon nanodots (C-dots) is proposed. The strategy is based on a one-step hydrothermal chemical reduction with thiourea and urea, leading to high quantum yield C-dots. The obtained C-dots are well-dispersed with a uniform size and a graphite-like structure. A synergistic reduction mechanism was investigated using Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The findings show that using both thiourea and urea during the one-pot synthesis enhances the luminescence of the generated C-dots. Moreover, the prepared C-dots have a high distribution of functional groups on their surface. In this work, C-dots proved to be a suitable nanomaterial for imaging of bacteria and exhibit potential for application in bioimaging thanks to their low cytotoxicity

Retrieving 3D Large Gradient Deformation Induced to Mining Subsidence Based on Fusion of Boltzmann Prediction Model and Single-Track InSAR Earth Observation Technology

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values

A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation

This paper proposes a novel hyperspectral image classification method that combines dynamic semi-supervised multiple-kernel collaborative representation ensemble selection with superpixel (SP) consistency constraints. The method is based on the consistency principle of labels within SP blocks, where the hyperspectral image is divided into different SP blocks, and each block is treated as an independent classification task. It applies a dynamic ensemble selection strategy to select high-confidence samples from the unlabeled data and assigns pseudo-labels to expand the available training sample set. Additionally, it employs a multiple-kernel collaborative representation classifier as the base classifier to better capture sample similarities and correlations, thereby improving the classification performance. Experimental results demonstrate that the proposed method achieves superior classification accuracy on various datasets such as Indian Pines, Purdue, and KSC, outperforming the traditional Meta-DES method significantly

Stability-Level Evaluation of the Construction Site above the Goaf Based on Combination Weighting and Cloud Model

Mineral resource-based cities have formed a large number of goafs due to the long-term mining of coal. It is of great significance to make full use of the abandoned land resources above the goaf to promote the transformation and development of resource-based cities. In order to avoid the threat of surface residual deformation to the proposed construction project, it is an urgent problem to obtain the stability results of the construction site accurately. First of all, based on the principles of relevance, hierarchy, representativeness and feasibility of index selection, 10 indexes are selected to construct the stability evaluation index system. Then the subjective weight and objective weight of evaluation indexes are determined based on improved AHP, rough set and CRITIC methods, which improves the accuracy of the determination of the index weights. In addition, the membership degree of each index is determined using the cloud model. Finally, the stability grade can be obtained according to the maximum membership degree theory. The above researches are applied to evaluate the stability of the Mianluan expressway construction site, and the results show that the stability level of the study area is not uniform and that there are two states: stable and basically stable. Finally, a sensitivity analysis of the subjective weight of each index is carried out, the index stopping time has the highest sensitivity to weight (12.44%), which is far lower than the corresponding weight change rate of 100%, indicating that the determination of weight is scientific and reasonable. These things considered, the reliability of the evaluation result is indirectly verified according to the field leveling. This research can provide a reference for the effective utilization of land resources above an old goaf

Un modelo de predicción de subsidencia minera en una capa gruesa y suelta basada en el modelo integral de probabilidad

The probability integral method is the most commonly used mining subsidence prediction model, but it is only applicable to ordinary geological mining conditions. When the loose layer in the geological mining conditions where the mining face is located is too thick, many inaccurate phenomena will occur when the movement deformation value is predicted by the probability integral method. The most obvious one is the problem that the predicted value converges too fast compared with the measured value in the edge of the sinking basin. In 2012, Wang and Deng proposed a modified model of probability integral method for the marginal errors in the model of probability integral method and verified the feasibility of the method through examples. In this paper, the method is applied to the prediction of surface movement under thick and loose layers after modified. Through practical application, it is found that due to the angle between the working face and the horizontal direction, the average mining depth in the strike direction is different from the average mining depth in the inclined direction, and the main influence radius of the two main sections are often. Therefore, based on this problem, this paper divides the main influence radius into trend and tendency and adjusts the parameters in the model to find the rules of the parameters. The original method uses a dynamic scale factor to adjust the predicted shape of the graph by adjusting the sinking coefficient. This study is aimed to set the scale factor to 0.5 and fix the value of the sinking factor, and propose to adjust the integral range and then adjust the shape of the graph to make it more in line with the actual measurement situation.El método integral de probabilidad es el modelo de predicción de subsidencia en minas más utilizado, pero solo es aplicable a las condiciones geológicas ordinarias de la minería. Cuando una capa suelta en las condiciones de extracción geológica donde se encuentra la cara de extracción es demasiado gruesa, el método integral de probabilidad no es exacto al predecir el valor de deformación del movimiento. El problema más obvio es que el valor predicho converge demasiado rápido en comparación con el valor medido en el borde de la cuenca de hundimiento. En 2012, Wang Zhengshuai propuso un modelo modificado del método integral de probabilidad para los errores marginales en el modelo del método integral de probabilidad y verificó la viabilidad del método a través de ejemplos. En este documento, el método se aplica a la predicción del movimiento de la superficie bajo capas gruesas y sueltas después de la modificación. A través de la aplicación práctica, se encuentra que debido al ángulo entre la cara de trabajo y la dirección horizontal, la profundidad promedio de extracción en la dirección de impacto es diferente a la profundidad promedio de extracción en la dirección inclinada, y frecuentemente al radio de influencia principal de las dos secciones principales. Por lo tanto, con base en este problema, este documento divide el radio de influencia principal en tendencia y ajusta los parámetros en el modelo para encontrar las reglas. El método original usa un factor de escala dinámico para ajustar la forma pronosticada del gráfico ajustando el coeficiente de hundimiento. Este estudio busca establecer el factor de escala en 0.5, fijar el valor del factor de hundimiento, ajustar el rango integral y luego ajustar la forma del gráfico para que esté más en línea con la situación de medición real

Investigación sobre el establecimiento de un modelo de predicción de subsidencia minera bajo una capa gruesa inestable y su método de inversión de parámetros

Most of the coal mining in China is underground, which will inevitably cause surface deformation and trigger a series of geological disasters. Therefore, it is essential to find a suitable method to forecast the ground sinking caused by underground mining. The most commonly used prediction model in China is the probability integral model (PIM). But when this model is used in the geological condition of mining under thick loose layers, the predicted edge of the sinking basin will converge faster than the actual measured sinking situation. A geometric model (GM) with a similar model shape as the PIM but with a larger boundary value was established in this paper to solve this problem. Then an improved cuckoo search algorithm (ICSA) was proposed in this paper to calculate the GM parameters. The stability and reliability of the ICSA were verified through a simulated working face. At last, the ICSA, in combination with the GM and the PIM, was used to fit 6 working faces with the geological mining condition of thick loose layers in the Huainan mining area. The results prove that GM can solve the above-mentioned PIM problem when it is used in geological mining conditions of thick loose layers. And it was obtained through comparative analysis that the GM and the PIM parameters can take the same value except for the main influence radius.La mayor parte de la minería del carbón en China es subterránea, lo que inevitablemente causa deformaciones en la superficie y desencadena desastres geológicos. Por lo tanto, es necesario encontrar un método adecuado para pronosticar el hundimiento del suelo causado por la minería subterránea. El modelo de predicción más utilizado en China es el modelo integral de probabilidad (PIM). Pero cuando este modelo se utiliza en la condición geológica de la minería bajo capas gruesas inetables, el borde previsto de la cuenca de hundimiento converge más rápido que la situación de hundimiento medida. Para resolver este problema, en este artículo se estableció un modelo geométrico (GM) que tiene una forma de modelo similar a la del PIM pero que tiene un valor límite mayor. En este trabajo se propuso un algoritmo de búsqueda de cuco mejorado (ICSA) para calcular los parámetros de GM, y se verificó la estabilidad y confiabilidad del ICSA a través de una frente de trabajo simulado. Por último, el ICSA en combinación con el GM y el PIM se utilizaron para ajustar 6 caras de trabajo con la condición de minería geológica de capas gruesas sueltas en el área minera de Huainan. Los resultados demuestran que el GM puede resolver el problema de PIM mencionado anteriormente cuando se utiliza en las condiciones de minería geológica de capas gruesas inestables. Y se obtuvo mediante análisis comparativo que los parámetros del GM y del PIM pueden tomar el mismo valor excepto por el radio de influencia principal